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Many dynamical processes of complex systems can be understood as the dynamicsof a group of nodes interacting on a given network structure. However, findingsuch interaction structure and node dynamics from time series of nodebehaviours is tough. Conventional methods focus on either network structureinference task or dynamics reconstruction problem, very few of them can workwell on both. This paper proposes a universal framework for reconstructingnetwork structure and node dynamics at the same time from observed time-seriesdata of nodes. We use a differentiable Bernoulli sampling process to generate acandidate network structure, and use neural networks to simulate the nodedynamics based on the candidate network. We then adjust all the parameters witha stochastic gradient descent algorithm to maximize the likelihood functiondefined on the data. The experiments show that our model can recover variousnetwork structures and node dynamics at the same time with high accuracy. Itcan also work well on binary, discrete and continuous time-series data, and thereconstruction results are robust against noise and missing information.

A Universal Framework for Reconstructing Complex Networks and Node Dynamics from Discrete or Continuous Dynamics Data